AIMC Topic: Fundus Oculi
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Automated Machine Learning versus Expert-Designed Models in Ocular Toxoplasmosis: Detection and Lesion Localization Using Fundus Images.
Ocular immunology and inflammation
Feb 27, 2024
PURPOSE: Automated machine learning (AutoML) allows clinicians without coding experience to build their own deep learning (DL) models. This study assesses the performance of AutoML in detecting and localizing ocular toxoplasmosis (OT) lesions in fund...
A cognitive deep learning approach for medical image processing.
Scientific reports
Feb 24, 2024
In ophthalmic diagnostics, achieving precise segmentation of retinal blood vessels is a critical yet challenging task, primarily due to the complex nature of retinal images. The intricacies of these images often hinder the accuracy and efficiency of ...
Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.
The British journal of ophthalmology
Feb 21, 2024
AIMS: To develop an algorithm to classify multiple retinal pathologies accurately and reliably from fundus photographs and to validate its performance against human experts.
Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging.
Scientific reports
Feb 13, 2024
Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson's disease and automate diagnostics would greatly improve the treatment of patients with Parkinson's disease. Current diagnosti...
Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net.
Network (Bristol, England)
Feb 12, 2024
Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an u...
DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age-related macular degeneration.
Acta ophthalmologica
Feb 12, 2024
OBJECTIVE: This study aimed to develop a deep learning (DL) model, named 'DeepAlienorNet', to automatically extract clinical signs of age-related macular degeneration (AMD) from colour fundus photography (CFP).
CoVi-Net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation.
Computers in biology and medicine
Jan 29, 2024
Retinal vessel segmentation plays a crucial role in the diagnosis and treatment of ocular pathologies. Current methods have limitations in feature fusion and face challenges in simultaneously capturing global and local features from fundus images. To...
M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images.
Network (Bristol, England)
Jan 27, 2024
Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification...
Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images.
Ophthalmology. Retina
Jan 26, 2024
OBJECTIVE: We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images.
Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis.
Ocular immunology and inflammation
Jan 23, 2024
PURPOSE: Retinal vasculitis (RV) is characterised by retinal vascular leakage, occlusion or both on fluorescein angiography (FA). There is no standard scheme available to segment RV features. We aimed to develop a deep learning model to segment both ...
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